Literature DB >> 23956328

Automated segmentation of pulmonary structures in thoracic computed tomography scans: a review.

Eva M van Rikxoort1, Bram van Ginneken.   

Abstract

Computed tomography (CT) is the modality of choice for imaging the lungs in vivo. Sub-millimeter isotropic images of the lungs can be obtained within seconds, allowing the detection of small lesions and detailed analysis of disease processes. The high resolution of thoracic CT and the high prevalence of lung diseases require a high degree of automation in the analysis pipeline. The automated segmentation of pulmonary structures in thoracic CT has been an important research topic for over a decade now. This systematic review provides an overview of current literature. We discuss segmentation methods for the lungs, the pulmonary vasculature, the airways, including airway tree construction and airway wall segmentation, the fissures, the lobes and the pulmonary segments. For each topic, the current state of the art is summarized, and topics for future research are identified.

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Year:  2013        PMID: 23956328     DOI: 10.1088/0031-9155/58/17/R187

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  29 in total

1.  Semiautomatic segmentation of longitudinal computed tomography images in a rat model of lung injury by surfactant depletion.

Authors:  Yi Xin; Gang Song; Maurizio Cereda; Stephen Kadlecek; Hooman Hamedani; Yunqing Jiang; Jennia Rajaei; Justin Clapp; Harrilla Profka; Natalie Meeder; Jue Wu; Nicholas J Tustison; James C Gee; Rahim R Rizi
Journal:  J Appl Physiol (1985)       Date:  2014-11-13

2.  Automatic lung segmentation using control feedback system: morphology and texture paradigm.

Authors:  Norliza M Noor; Joel C M Than; Omar M Rijal; Rosminah M Kassim; Ashari Yunus; Amir A Zeki; Michele Anzidei; Luca Saba; Jasjit S Suri
Journal:  J Med Syst       Date:  2015-02-10       Impact factor: 4.460

Review 3.  Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends.

Authors:  Awais Mansoor; Ulas Bagci; Brent Foster; Ziyue Xu; Georgios Z Papadakis; Les R Folio; Jayaram K Udupa; Daniel J Mollura
Journal:  Radiographics       Date:  2015 Jul-Aug       Impact factor: 5.333

4.  HOSVD-Based 3D Active Appearance Model: Segmentation of Lung Fields in CT Images.

Authors:  Qingzhu Wang; Wanjun Kang; Haihui Hu; Bin Wang
Journal:  J Med Syst       Date:  2016-06-08       Impact factor: 4.460

5.  Artificial Intelligence in COPD: New Venues to Study a Complex Disease.

Authors:  Raúl San José Estépar
Journal:  Barc Respir Netw Rev       Date:  2020 May-Dec

6.  Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries.

Authors:  Mitchel J Colebank; L Mihaela Paun; M Umar Qureshi; Naomi Chesler; Dirk Husmeier; Mette S Olufsen; Laura Ellwein Fix
Journal:  J R Soc Interface       Date:  2019-10-02       Impact factor: 4.118

7.  Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study.

Authors:  Daniel F Polan; Samuel L Brady; Robert A Kaufman
Journal:  Phys Med Biol       Date:  2016-08-17       Impact factor: 3.609

8.  Pulmonary Lobe Segmentation With Probabilistic Segmentation of the Fissures and a Groupwise Fissure Prior.

Authors:  Felix J S Bragman; Jamie R McClelland; Joseph Jacob; John R Hurst; David J Hawkes
Journal:  IEEE Trans Med Imaging       Date:  2017-04-18       Impact factor: 10.048

9.  An approach for reducing the error rate in automated lung segmentation.

Authors:  Gurman Gill; Reinhard R Beichel
Journal:  Comput Biol Med       Date:  2016-06-29       Impact factor: 4.589

10.  Inter-observer Variability Analysis of Automatic Lung Delineation in Normal and Disease Patients.

Authors:  Luca Saba; Joel C M Than; Norliza M Noor; Omar M Rijal; Rosminah M Kassim; Ashari Yunus; Chue R Ng; Jasjit S Suri
Journal:  J Med Syst       Date:  2016-04-25       Impact factor: 4.460

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